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Prospective Collection and Registry Study of Multicenter, Multidisciplinary Surgical Minimally Invasive Videos (VISION)

5. august 2026 oppdatert av: Zeyu Zhang, PHD, Chinese Academy of Sciences

Prospective Observational Cohort Study on the Construction of Standardized Video Datasets for Multicenter, Multidisciplinary Minimally Invasive Laparoscopic and Robotic Surgery and Their Application in the Development of Surgical AI Large Models

This is a prospective multicenter patient registry study. We continuously collect full-length intraoperative surgical videos from thoracoscope, laparoscope, hysteroscope, transcervical resectoscope, cystoscope, prostate resectoscope, arthroscope, intervertebral foramen endoscope, otorhinolaryngology endoscope and endoscopic surgical robots, accompanied by inpatient medical records, preoperative imaging data and 5-year postoperative follow-up data. All imaging data will be standardized and de-identified to construct a large-scale standardized surgical video dataset. The dataset will be applied for training, verification and optimization of surgical video foundation large model, serving for surgical teaching, intraoperative operation quality control and basic medical AI research. We will also explore the correlation between intraoperative surgical details and postoperative prognosis to improve the standard specifications of minimally invasive surgery. No clinical intervention will be imposed on participants throughout the whole research.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Studietype

Observasjonsmessig

Registrering (Antatt)

2000

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Navn: KUNSHAN HE
Telefonnummer: +86 18500535530
E-post: hekunshan@buaa.edu.cn

Studiesteder

Kina
- - Beijing, Kina
    - Institute of Automation, Chinese Academy of Sciences

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

Voksen
Eldre voksen

Tar imot friske frivillige

Nei

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

This prospective multicenter observational cohort study will enroll a total of 2,000 inpatients undergoing minimally invasive endoscopic, laparoscopic, or robotic surgery across multiple departments and participating medical centers.

Beskrivelse

Inclusion Criteria:

Patients aged ≥ 18 years old hospitalized to receive minimally invasive endoscopic, laparoscopic or robotic surgical treatment for diseases of various body systems;
Complete full-length intraoperative surgical videos can be recorded during operation, with complete medical records and preoperative imaging data;
Participants fully understand the study, voluntarily sign written informed consent, and agree that their de-identified intraoperative images and clinical data can be used for scientific research.

Exclusion Criteria:

Minors under 18 years of age;
Patients with incomplete intraoperative videos or missing clinical imaging documents;
Patients with consciousness disturbance or mental disorders who cannot sign informed consent independently;
Subjects who refuse to participate in the study and disapprove the use of their medical data for research;
Patients who are predicted to be unavailable for long-term postoperative follow-up.

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Hva måler studien?

Primære resultatmål

Resultatmål	Tidsramme
Completion rate of qualified intraoperative surgical imaging data Tidsramme: Immediately after each surgery	Immediately after each surgery

Sekundære resultatmål

Resultatmål	Tiltaksbeskrivelse	Tidsramme
Completeness rate of long-term postoperative clinical follow-up Tidsramme: 3 months, 1 year, 3 years and 5 years after surgery		3 months, 1 year, 3 years and 5 years after surgery
Reusability rate of annotated key anatomical structures in videos Tidsramme: From completion of data warehousing and annotation, the reusability rate will be analyzed within 3 months, assessed up to 3 months after annotation completion.		From completion of data warehousing and annotation, the reusability rate will be analyzed within 3 months, assessed up to 3 months after annotation completion.
Feasibility rate (%) of surgical video dataset applied in different clinical AI research scenarios Tidsramme: After full construction of the surgical video dataset, scenario feasibility assessment will be finished within 6 months, assessed up to 6 months after dataset construction.	Three core application scenarios are predefined: 1) training of surgical computer vision AI models; 2) validation of intraoperative surgical recognition algorithms; 3) surgical skill assessment and teaching research. An expert review panel consisting of at least 3 attending surgeons and 2 medical AI researchers independently evaluates whether the dataset has sufficient sample size, annotation completeness and video quality to support each scenario. Feasibility proportion is calculated as: (Number of scenarios the dataset is suitable for / Total predefined scenarios) × 100%.	After full construction of the surgical video dataset, scenario feasibility assessment will be finished within 6 months, assessed up to 6 months after dataset construction.
Accuracy percentage (%) of AI-based surgical procedure identification on annotated surgical videos Tidsramme: After completion of data warehousing and annotation, AI surgical procedure identification accuracy testing will be conducted within 3 months, assessed up to 3 months post annotation completion.	After all surgical videos are imported into the data warehouse and manually annotated by experienced surgeons to generate gold-standard procedure labels, the surgical video analysis AI model automatically outputs predicted surgical procedure categories for each video clip. Each AI-predicted label is compared against the manual gold-standard annotation label. Identification accuracy is calculated by the formula: (Number of video clips with correctly predicted surgical procedures / Total number of tested video clips) × 100%.	After completion of data warehousing and annotation, AI surgical procedure identification accuracy testing will be conducted within 3 months, assessed up to 3 months post annotation completion.

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Chinese Academy of Sciences

Samarbeidspartnere

Beihang University

Beijing Digital Precision Medicine Technology Co., Ltd.

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Antatt)

1. august 2026

Primær fullføring (Antatt)

31. juli 2031

Studiet fullført (Antatt)

31. juli 2032

Datoer for studieregistrering

Først innsendt

31. juli 2026

Først innsendt som oppfylte QC-kriteriene

5. august 2026

Først lagt ut (Faktiske)

7. august 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)